FacialPulse：一种基于时序面部标志的高效RNN抑郁症检测方法
FacialPulse: An Efficient RNN-based Depression Detection via Temporal Facial Landmarks
AI总结：
本文提出FacialPulse框架，通过面部运动建模模块和面部标志校准模块高效捕获时序面部特征，在抑郁症检测中实现精度与速度的双重提升。
摘要：
抑郁症是一种普遍存在的心理健康障碍，对个人的生活和幸福感有显著影响。早期检测和干预对于抑郁症的有效治疗和管理至关重要。近年来，有许多端到端的深度学习方法利用面部表情特征进行自动抑郁症检测。然而，当前大多数方法忽视了面部表情的时间动态。尽管最近的3DCNN方法弥补了这一不足，但由于选择了基于CNN的主干网络和冗余的面部特征，它们带来了更高的计算成本。为了解决上述局限性，考虑到面部表情的时间相关性，我们提出了一种名为FacialPulse的新框架，该框架能够以高精度和高速度识别抑郁症。通过利用双向特性并熟练处理长期依赖关系，在FacialPulse中设计了面部运动建模模块（FMMM）以充分捕获时间特征。由于所提出的FMMM具有并行处理能力，并具有门控机制以缓解梯度消失，该模块还可以显著提升训练速度。此外，为了有效利用面部标志替代原始图像以减少信息冗余，设计了面部标志校准模块（FLCM）以消除面部标志误差，从而进一步提高识别精度。在AVEC2014数据集和MMDA数据集（一个抑郁症数据集）上的大量实验证明了FacialPulse在识别精度和速度上的优越性，与基线相比，平均MAE（平均绝对误差）降低了21%，与最先进的方法相比，识别速度提高了100%。代码已发布在https://github.com/volatileee/FacialPulse。
英文摘要：
Depression is a prevalent mental health disorder that significantly impacts individuals' lives and well-being. Early detection and intervention are crucial for effective treatment and management of depression. Recently, there are many end-to-end deep learning methods leveraging the facial expression features for automatic depression detection. However, most current methods overlook the temporal dynamics of facial expressions. Although very recent 3DCNN methods remedy this gap, they introduce more computational cost due to the selection of CNN-based backbones and redundant facial features. To address the above limitations, by considering the timing correlation of facial expressions, we propose a novel framework called FacialPulse, which recognizes depression with high accuracy and speed. By harnessing the bidirectional nature and proficiently addressing long-term dependencies, the Facial Motion Modeling Module (FMMM) is designed in FacialPulse to fully capture temporal features. Since the proposed FMMM has parallel processing capabilities and has the gate mechanism to mitigate gradient vanishing, this module can also significantly boost the training speed. Besides, to effectively use facial landmarks to replace original images to decrease information redundancy, a Facial Landmark Calibration Module (FLCM) is designed to eliminate facial landmark errors to further improve recognition accuracy. Extensive experiments on the AVEC2014 dataset and MMDA dataset (a depression dataset) demonstrate the superiority of FacialPulse on recognition accuracy and speed, with the average MAE (Mean Absolute Error) decreased by 21% compared to baselines, and the recognition speed increased by 100% compared to state-of-the-art methods. Codes are released at https://github.com/volatileee/FacialPulse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
